Discovering genomic rearrangements under selection in serious ovarian cancer

复发性基因融合和内部串联重复是最具肿瘤特异性的分子机制之一。 这些标记物是已知的，可以提供潜在的治疗靶点。除了一些明显的例外， 然而，相对常见的复发性基因融合尚未在常见的 癌症，通常具有多个复杂的染色体重排，难以通过 传统的细胞遗传学方法。复杂的肿瘤核型使得难以使用 细胞遗传学，但建议的可能性，经常性重排产生融合或内部串联 重复（ITD）可能是普遍的。该提案旨在使用深度测序和新的分析方法， 描述了研究浆液性卵巢癌基因组和转录组方面的技术， 由于技术或分析方法的限制， 对肿瘤的选择性压力。这一建议的方面如下：1）进一步调查 卵巢癌中基因重排的程度，重点是发现局部重排 转录成RNA; 2）确定一组新的环状转录物的组成，我有 最近发现在正常和致病人类细胞中以相对高的水平表达; 3） 描述卵巢癌中的双分钟，结合生物信息学来确定卵巢癌中的重排。 它们的序列组成和统计分析，以确定其组成的进化压力 由肿瘤产生。申请人在发现新的基因融合体方面有成功的记录， 超高通量测序（ESRRA-C11 orf 20融合），以及设计原始的严格的 超高通量数据的统计和生物信息学方法。在帕特里克奥博士的指导下，布朗，在高通量基因组技术和分析它们的统计方法的先驱，申请人将继续职业发展和培训。该项目的第一个目标将在辅导阶段实现，目标2和3的实验将进行试点。K99/R 00奖将支持申请人发展成为独立调查员。

Recurrent gene fusions and internal tandem duplications are among the most tumor-specific molecular markers known and can provide the potential for therapeutic targets. With a few notable exceptions, however, relatively common recurrent gene fusions have not been identified in commonly occurring carcinomas, which often have multiple, complex chromosomal rearrangements that are difficult to analyze by traditional cytogenetic approaches. Complex tumor karyotpes make it difficult to identify gene fusions using cytogenetics, but suggest the possibility that recurrent rearrangements producing fusions or internal tandem duplications (ITDs) may be prevalent. This proposal aims to use deep sequencing and the novel analytic techniques described to study aspects of the serous ovarian cancer genome and transcriptome which have remained hidden due to limitations in technology or analytical methods, and to test intra-individual and inter-individual selective pressures on tumors. The aspects of this proposal are as follows 1) to further investigate the extent of gene rearrangements in ovarian cancer, focusing on discovering local rearrangements transcribed into RNA; 2) to determine the composition of a group of novel circular transcripts that I have recently found to be expressed at relatively high levels in normal and pathogenic human cells; 3) to characterize double minutes in ovarian cancer, combining bioinformatics to determine rearrangements in their sequence composition and statistical analysis to determine evolutionary pressures on their composition exerted by the tumors. The applicant has a track-record of success in discovering novel gene fusions with ultra-high throughput sequencing (the ESRRA-C11 orf20 fusion), as well as designing original rigorous statistical and bioinformatic methods for ultra-high throughput data. Under the mentorship of Dr. Patrick O. Brown, a pioneer in high throughput genomic technologies and statistical methods for analyzing them, the applicant will continue career development and training. The first aim of this project will be performed during the mentoring phase, and experiments for aims 2 and 3 will be piloted. The K99/R00 award will support the applicant in her development into an independent investigator.